Matches in ScholarlyData for { ?s ?p ?o. }
- item-3 hasContent floriana-esposito.
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- 334 creator america-holloway.
- 334 creator chaitanya-chemudugunta.
- 334 creator mark-steyvers.
- 334 creator padhraic-smyth.
- 334 type InProceedings.
- 334 label "Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning".
- 334 sameAs 334.
- 334 abstract "Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose uses statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.".
- 334 hasAuthorList authorList.
- 334 hasTopic Semantic_Web.
- 334 isPartOf proceedings.
- 334 keyword "ontologies".
- 334 keyword "tagging".
- 334 keyword "topic models".
- 334 keyword "unsupervised learning".
- 334 title "Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning".
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- item-1 hasContent chaitanya-chemudugunta.
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- item-2 hasContent america-holloway.
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- item-3 hasContent padhraic-smyth.
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- item-4 hasContent mark-steyvers.
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- 341 creator bijan-parsia.
- 341 creator pavel-klinov.
- 341 type InProceedings.
- 341 label "Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach".
- 341 sameAs 341.
- 341 abstract "This paper describes the first steps towards developing a methodology for testing and evaluating the performance of reasoners for the probabilistic description logic P-SHIQ(D). Since P-SHIQ(D) is a relatively new formalism for handling uncertainty in DL knowledge bases, no such methodology has been proposed so far. Moreover there are no sufficiently large probabilistic ontologies to be used as test suites. In addition, since the reasoning services in P-SHIQ(D) are mostly query oriented, there is no single problem (like classification or realization in classical DL) that could be an obvious candidate for benchmarking. All these issues make it hard to evaluate the performance of reasoners, reveal the complexity bottlenecks and assess the effect of optimization strategies. This paper aims at alleviating these important problems by making the following contributions: First, it describes a probabilistic ontology that has been developed for the real-life domain of breast cancer and which poses significant challenges for the state-of-art P-SHIQ(D) reasoners. Second, it explains a systematic approach to generating a series of probabilistic reasoning problems that allow to evaluate the reasoning performance and shed light on what makes reasoning in P-SHIQ(D) hard in practice. Finally, the paper explains the optimization strategy that helps to overcome some of the difficulties of reasoning revealed during the experiments. The impact of the strategy is also demonstrated using the developed evaluation methodology.".
- 341 hasAuthorList authorList.
- 341 hasTopic Semantic_Web.
- 341 isPartOf proceedings.
- 341 keyword "Description Logic".
- 341 keyword "Evaluation".
- 341 keyword "Optimization".
- 341 keyword "Probabilistic Reasoning".
- 341 title "Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach".
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- item-1 hasContent pavel-klinov.
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- item-2 hasContent bijan-parsia.
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- 342 creator bettina-bauer-messmer.
- 342 creator rolf-gruetter.
- 342 creator thomas-scharrenbach.
- 342 type InProceedings.
- 342 label "Improving an RCC-derived Geospatial Approximation by OWL Axioms".
- 342 sameAs 342.
- 342 abstract "An approach to improve an RCC-derived geospatial approximation is presented which makes use of OWL class inclusion axioms. The algorithm used to control the approximation combines hypothesis testing with consistency checking provided by a knowledge representation system based on description logics. Propositions about the consistency of the refined ABox w.r.t. the associated TBox when compared to baseline ABox and TBox are made. Formal proves of the divergent consistency results when checking either of both are provided. The application of the approach to a geospatial setting results in a roughly tenfold improved approximation when using the refined ABox and TBox. Ways to further improve the approximation and to automate the detection of falsely calculated relations are discussed.".
- 342 hasAuthorList authorList.
- 342 hasTopic Semantic_Web.
- 342 isPartOf proceedings.
- 342 keyword "Consistency Checking".
- 342 keyword "Geospatial Approximation".
- 342 keyword "Hypothesis Testing".
- 342 keyword "Region Connection Calculus".
- 342 keyword "Web Ontology Language".
- 342 title "Improving an RCC-derived Geospatial Approximation by OWL Axioms".
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- item-1 hasContent rolf-gruetter.
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- item-2 hasContent thomas-scharrenbach.
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- item-3 hasContent bettina-bauer-messmer.
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- 347 creator markus-kroetzsch.
- 347 creator pascal-hitzler.
- 347 creator sebastian-rudolph.
- 347 type InProceedings.